ArticleScientific reports2020
Non-invasive identification of swallows via deep learning in high resolution cervical auscultation recordings.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06637774 (The Aspirometer), which is not on this map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
The Aspirometer: A Noninvasive Tool for Detecting Aspiration
Who cites it
35 citing papers in PubMed, 77 citations in OpenAlex.
- Acoustic and Perceptual Parameters of Swallow Sounds Associated with and Without Aspiration in Preterm Neonates: A Pilot Study.Dysphagia · 2026Article
- Diagnostic Accuracy of Cervical Auscultation for Aspiration Detection in Adult Dysphagia: Implications for FEES Prioritization.Laryngoscope investigative otolaryngology · 2026Article
- Enhancing the assessment of deglutition function in preterm infants with mechano-acoustic analysis and machine learning: a narrative review.Perspectives of the ASHA special interest groups · 2026Article
- Using Machine Learning for the Automated Segmentation and Detection of Swallows Obtained by Digital Cervical Auscultation in Preterm Neonates.Dysphagia · 2026Article
- Sounding out Dysphagia: Using a Digital Stethoscope to Assess Audiometric Differences in Swallowing.The Laryngoscope · 2026Article
- Use of Transfer Learning for the Automated Segmentation and Detection of Swallows via Digital Cervical Auscultation in Children.Dysphagia · 2025Article
- Novel Videographic-Free Framework for Tracking Anatomical Structures Using Swallowing Accelerometer Signals and Multi-task Transformers.Journal of healthcare informatics research · 2025Article
- Non-invasive integrated swallowing kinematic analysis framework leveraging transformer-based multi-task neural networks.Computers in biology and medicine · 2025Article
- Digital health technologies in swallowing care from screening to rehabilitation: A narrative review.Auris, nasus, larynx · 2025Review
- Article
- sEMG-based automatic characterization of swallowed materials.Biomedical engineering online · 2024Article
- Novel Approach Combining Shallow Learning and Ensemble Learning for the Automated Detection of Swallowing Sounds in a Clinical Database.Sensors (Basel, Switzerland) · 2024Article
- Elevating Patient Care With Deep Learning: High-Resolution Cervical Auscultation Signals for Swallowing Kinematic Analysis in Nasogastric Tube Patients.IEEE journal of translational engineering in health and medicine · 2024Article
- Quantifying Healthy Aging in Older Veterans Using Computational Audio Analysis.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Article
- Pilot Study: Magnetic Motion Analysis for Swallowing Detection Using MEMS Cantilever Actuators.Sensors (Basel, Switzerland) · 2023Article
- Autonomous Swallow Segment Extraction Using Deep Learning in Neck-Sensor Vibratory Signals From Patients With Dysphagia.IEEE journal of biomedical and health informatics · 2023Article
- Non-Invasive Sensor-Based Estimation of Anterior-Posterior Upper Esophageal Sphincter Opening Maximal Distension.IEEE journal of translational engineering in health and medicine · 2023Article
- Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia.Frontiers in neuroscience · 2023Article
- Swallow Detection with Acoustics and Accelerometric-Based Wearable Technology: A Scoping Review.International journal of environmental research and public health · 2022Article
- AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
High resolution cervical auscultation is a very promising noninvasive method for dysphagia screening and aspiration detection, as it does not involve the use of harmful ionizing radiation approaches. Automatic extraction of swallowing events in cervical auscultation is a key step for swallowing analysis to be clinically effective. Using time-varying spectral estimation of swallowing signals and deep feed forward neural networks, we propose an automatic segmentation algorithm for swallowing accelerometry and sounds that works directly on the raw swallowing signals in an online fashion. The algorithm was validated qualitatively and quantitatively using the swallowing data collected from 248 patients, yielding over 3000 swallows manually labeled by experienced speech language pathologists. With a detection accuracy that exceeded 95%, the algorithm has shown superior performance in comparison to the existing algorithms and demonstrated its generalizability when tested over 76 completely unseen swallows from a different population. The proposed method is not only of great importance to any subsequent swallowing signal analysis steps, but also provides an evidence that such signals can capture the physiological signature of the swallowing process.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.